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AI writeback is currently in Beta and gated by a feature flag. Contact Lightdash support to enable it for your organization.
AI writeback lets you ask an AI agent in chat or Slack to change something in the dbt repository that backs your project — rename a metric, add a dimension, edit a model’s SQL, fix a YAML description — and have the agent open a pull request (or GitLab merge request) with the change. This is the same writeback capability you would normally trigger from the Custom Metric or SQL Runner menus, surfaced inside the conversation with your AI agent.

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When to use it

Use AI writeback when the change you want needs to land in your dbt project files:
  • Rename a metric in a YAML file
  • Add a new metric or dimension to a dbt model
  • Edit a model’s SQL
  • Update a description, label, or meta block
  • Fix a typo in a column definition
For other kinds of changes, the agent will pick a different tool:

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Prerequisites

  • The AI writeback feature flag must be enabled for your organization.
  • Your project’s dbt connection must be a GitHub or GitLab repository. Bitbucket, Azure DevOps, dbt Cloud, and local dbt projects are not supported.
  • The matching Lightdash app must be installed for the repository so the agent can open requests:
    • GitHub — install the Lightdash GitHub App on the repository.
    • GitLab — connect the Lightdash GitLab App for your organization (self-hosted GitLab is supported via the project’s host_domain).
  • You need at least project Developer permissions on the project.

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How it works

When you ask the agent for a change that belongs in the repo, it calls a tool called proposeWriteback. The tool:
  1. Spawns a separate, sandboxed writeback agent with no memory of your chat.
  2. Hands that agent a self-contained instruction generated from your request.
  3. The writeback agent edits the relevant files, runs lightdash compile to validate the result, and pushes a new branch.
  4. A pull request (GitHub) or merge request (GitLab) is opened against your repository’s default branch and the URL is returned to you in chat.
The tool call is synchronous and can take a few minutes. The agent will tell you a pull request is being prepared while it waits.
GitHub commits are signed by the Lightdash GitHub App. GitLab commits are pushed over OAuth and are unsigned.

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Using it in chat

Phrase your request as a direct change to the repo and, where possible, name the file, model, or field you want touched. The more specific you are, the more reliably the writeback agent finds the right place to edit. Good prompts
Less effective prompts
When the pull request is ready, the agent posts the URL in the thread. Open it, review the diff, and merge as you would any other pull request. After merging, refresh dbt in Lightdash (or let your CI/CD do it) so the change takes effect.

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Iterating on an existing pull request

If you’ve already opened a writeback pull request — either earlier in the same thread or by pasting a link to one — the agent commits follow-up changes onto that PR’s branch instead of opening a new one. The PR’s title and description are refreshed to reflect the latest change. There are two ways this happens:
  • Same thread. If the thread already has a writeback PR, any further writeback requests in that thread automatically resume it. You don’t need to paste the link.
  • Paste a link. In a new thread (or before a writeback PR exists for the current thread), paste the GitHub pull request URL alongside your request. The agent picks up the link, checks out that PR’s branch, and commits your edits onto it.
The pasted pull request must:
  • Live in the same GitHub repository as the project’s dbt connection.
  • Be open — merged or closed PRs are rejected.
  • Have its branch in the same repository (PRs opened from forks are rejected).
If any of those checks fail, the agent stops and tells you why instead of silently opening a new pull request.

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What happens if it can’t run

  • dbt write-back — write back individual custom metrics, dimensions, and SQL Runner queries from the Lightdash UI.